Adam Bisno. <i>Big Business and the Crisis of German Democracy: Liberalism and the Grand Hotels of Berlin, 1875–1933</i>.
Bibliographic record
Abstract
Detractors of microhistory frequently point to its potential exceptionalism. What is to say that any one case study is representative? The small does not necessarily help us understand the large. Proponents of this methodology, on the other hand, see in its supposed “smallness” its distinct advantage: Scholars come to understand the particular case’s sui generis; in its idiosyncrasies lie richness and explanatory potential. Replete with remarkable detail, from the fraying bedsheets to the separation of bone from gristle in the kitchen, Adam Bisno’s fascinating account in Big Business and the Crisis of German Democracy demonstrates the strengths of microhistory. Berlin’s grand hotels were established in that era of optimism and wealth that followed the founding of the German Empire. Like many capital-intensive projects at that time, funds were raised through joint stock companies, one important way that the up-and-coming middle classes exerted financial power when political power still eluded them. By 1900, Berlin had eight grand hotels, all of them breathtakingly ornate and located in the burgeoning city center, where their guests could enjoy the many consumer diversions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".